Benvinguts al Repositori Digital de la UPF

Automatic tonic identification in Indian art music: approaches and evaluation

Mostra el registre parcial de l'element

dc.contributor.author Gulati, Sankalp
dc.contributor.author Bellur, Ashwin
dc.contributor.author Salamon, Justin
dc.contributor.author Ranjani, H. G.
dc.contributor.author Ishwar, Vignesh
dc.contributor.author Murthy, Hema A.
dc.contributor.author Serra, Xavier
dc.date.accessioned 2016-01-28T08:12:30Z
dc.date.available 2016-01-28T08:12:30Z
dc.date.issued 2014
dc.identifier.citation Gulati S, Bellur A, Salamon J, Ranjani HG, Ishwar V, Murthy HA, Serra X. Automatic tonic identification in Indian art music: approaches and evaluation. Journal of New Music Research. 2014; 43(1): 55–73. DOI 10.1080/09298215.2013.875042
dc.identifier.issn 0929-8215
dc.identifier.uri http://hdl.handle.net/10230/25675
dc.description.abstract The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rāg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rāg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.
dc.description.sponsorship This work is partly supported by the European Research Council/nunder the European Union’s Seventh Framework Program, as/npart of the CompMusic project (ERC grant agreement 267583).
dc.language.iso eng
dc.publisher Taylor & Francis (Routledge)
dc.relation.ispartof Journal of New Music Research. 2014; 43(1): 55–73.
dc.rights © Taylor & Francis. This is an electronic version of an article published in [Gulati S, Bellur A, Salamon J, Ranjani HG, Ishwar V, Murthy HA, Serra X. Automatic tonic identification in Indian art music: approaches and evaluation. Journal of New Music Research. 2014;43(01):55–73.]. [Journal of New Music Research] is available online at: http://www.tandfonline.com/doi/abs/10.1080/09298215.2013.875042.
dc.title Automatic tonic identification in Indian art music: approaches and evaluation
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1080/09298215.2013.875042
dc.subject.keyword Tonic
dc.subject.keyword Drone
dc.subject.keyword Indian art music
dc.subject.keyword Hindustani
dc.subject.keyword Carnatic
dc.subject.keyword Tanpura
dc.subject.keyword Sadja
dc.subject.keyword Indian classical music
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/267583
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


Aquest element apareix en la col·lecció o col·leccions següent(s)

Mostra el registre parcial de l'element